Chest wall perforator flaps are safe and can decrease mastectomy rates in breast cancer surgery: multicentre cohort study
Bibliographic record
Abstract
BACKGROUND: Chest wall perforator flaps are emerging in oncoplastic breast conservation, mostly as an alternative to mastectomy. However, standardization and consensus on patient selection, techniques, and outcomes have not yet been reached. The aim of this international multicentre collaborative study was to explore practice patterns and outcomes in high-volume centres from different countries. METHODS: Patients with both pre-invasive and invasive breast cancer treated at the Uppsala University Hospital in Uppsala, Sweden, the Royal Marsden Hospital in London, UK, and the Westmead Breast Cancer Institute in Sydney, Australia, were included in this study. The rationale for offering chest wall perforator flaps and surgical outcomes were prospectively documented. RESULTS: In total, 603 patients were analysed median age of 54 (interquartile range (i.q.r.) 48-63) years, median BMI of 25.0 (i.q.r. 22.5-28.1) kg/m2, median tumour extent of 30 (IQR 19-45) mm, median breast volume of 280 (i.q.r. 216-430) ml, and median calculated resection ratio of 16% (i.q.r. 9%-28%). In 67.7%, the treating surgeon had offered chest wall perforator flaps to avoid mastectomy. The procedure was performed as day surgery in 69.5% of patients, with an overall complication rate of 8.6% and the majority of complications being classified as Clavien-Dindo grade I (5.3% of patients). The re-excision rate was 15.9%, with only 1.5% of patients converting to a mastectomy. There were no flap losses. At a median follow-up of 22 (range 12 to 98) months, rates of local recurrence, distant recurrence, and breast cancer-related mortality were 1.9%, 4.9%, and 1.7% respectively. CONCLUSION: Chest wall perforator flaps are a useful option to allow more women to avoid mastectomy. In experienced hands, the procedure is safe and should be offered to suitable patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".